Experiment in Onboard Synthetic Aperture Radar Data Processing

Single event upsets (SEUs) are a threat to any computing system running on hardware that has not been physically radiation hardened. In addition to mandating the use of performance-limited, hardened heritage equipment, prior techniques for dealing with the SEU problem often involved hardware-based error detection and correction (EDAC). With limited computing resources, software- based EDAC, or any more elaborate recovery methods, were often not feasible. Synthetic aperture radars (SARs), when operated in the space environment, are interesting due to their relevance to NASAs objectives, but problematic in the sense of producing prodigious amounts of raw data. Prior implementations of the SAR data processing algorithm have been too slow, too computationally intensive, and require too much application memory for onboard execution to be a realistic option when using the type of heritage processing technology described above. This standard C-language implementation of SAR data processing is distributed over many cores of a Tilera Multicore Processor, and employs novel Radiation Hardening by Software (RHBS) techniques designed to protect the component processes (one per core) and their shared application memory from the sort of SEUs expected in the space environment. The source code includes calls to Tilera APIs, and a specialized Tilera compiler is required to produce a Tilera executable. The compiled application reads input data describing the position and orientation of a radar platform, as well as its radar-burst data, over time and writes out processed data in a form that is useful for analysis of the radar observations

Robust learning with anytime-guaranteed feedback

Under data distributions which may be heavy-tailed, many stochastic gradient-based learning algorithms are driven by feedback queried at points with almost no performance guarantees on their own. Here we explore a modified "anytime online-to-batch" mechanism which for smooth objectives admits high-probability error bounds while requiring only lower-order moment bounds on the stochastic gradients. Using this conversion, we can derive a wide variety of "anytime robust" procedures, for which the task of performance analysis can be effectively reduced to regret control, meaning that existing regret bounds (for the bounded gradient case) can be robustified and leveraged in a straightforward manner. As a direct takeaway, we obtain an easily implemented stochastic gradient-based algorithm for which all queried points formally enjoy sub-Gaussian error bounds, and in practice show noteworthy gains on real-world data applications

Development of a Reagentless Amperometric Ethanol Biosensor Based on Yeast Alcohol Dehydrogenase and its Coenzyme, NAD+, Coimmobilized on a Carbon Nanotubes- modified Electrode

A reagentless amperometric ethanol biosensor was fabricated by modifying a glassy carbon (GC) electrode with a thin film of multi-walled carbon nanotubes (MWNTs) and depositing yeast alcohol dehydrogenase (YADH) and its coenzyme, nicotinamide adenine dinucleotide (NAD+), on the surface of the modified electrode. The enzyme was immobilized on the modified electrode using two techniques: adsorption and covalent attachment. Biosensors based on graphite and carbon nanofibers (CNFs) were also fabricated in a similar manner except that the enzyme was only adsorbed to the electrode surface. The performance of the biosensors was assessed using a number of analytical techniques. Cyclic voltammetry was employed to determine the peak potential of NADH oxidation for each biosensor. Amperometric measurements were then conducted at or near the peak potential and the current response of each biosensor to successive ethanol additions was evaluated. The two MWNT-based biosensors to successive ethanol additions was evaluated. The two MWNT-based biosensors with adsorbed and covalently attached YADH were subjected to more detailed analysis including evaluation of stability, reusability and linear concentration range. The MWNT-based biosensor was found to exhibit a much higher current response to ethanol than the graphite- and CNF-based biosensors at a working potential of +0.3 V (vs. Ag/AgCl). In addition, it displayed a relatively quick and stable response to individual ethanol additions. Both the adsorbed and covalently attached MWNT-biosensors had large linear concentration ranges, excellent stability and similar reusabilities